Microsoft Fabric: Lakehouse (Notes)

Disclaimer: This is work in progress intended to consolidate information from various sources. 

Last updated: 10-Mar-2024

Lakehouse

• a unified platform that combines the capabilities of 
• data lake
• built on top of the OneLake scalable storage layer using and Delta format tables [1]
• support ACID  transactions through Delta Lake formatted tables for data consistency and integrity [1]
• ⇒ scalable analytics solution that maintains data consistency [1]
• {capability} scalable, distributed file storage
• can scale automatically and provide high availability and disaster recovery [1]
• {capability} flexible schema-on-read semantics
• ⇒ the schema can be changed as needed [1]
• ⇐ rather than having a predefined schema
• {capability} big data technology compatibility
• store all data formats 
• can be used with various analytics tools and programming languages
• use Spark and SQL engines to process large-scale data and support machine [1] learning or predictive modeling analytics
• data warehouse
• {capability} relational schema modeling
• {capability} SQL-based querying
• {feature} has a built-in SQL analytics endpoint
• ⇐ the data can be queried by using SQL without any special setup [2]
• {capability} proven basis for analysis and reporting
• ⇐ unlocks data warehouse capabilities without the need to move data [2]
• ⇐ a database built on top of a data lake 
• ⇐  includes metadata
• ⇐ a data architecture platform for storing, managing, and analyzing structured and unstructured data in a single location [2]
• ⇒ single location for data engineers, data scientists, and data analysts to access and use data [1]
• ⇒ it can easily scale to large data volumes of all file types and sizes
• ⇒ it's easily shared and reused across the organization
• supports data governance policies [1]
• e.g. data classification and access control
• can be created in any premium tier workspace [1]
• appears as a single item within the workspace in which was created [1]
• ⇒ access is controlled at this level as well [1]
• directly within Fabric
• via the SQL analytics endpoint
• permissions are granted either at the workspace or item level [1]
• users can work with data via
• lakehouse UI
• add and interact with tables, files, and folders [1]
• SQL analytics endpoint 
• enables to use SQL to query the tables in the lakehouse and manage its relational data model [1]
• two physical storage locations are provisioned automatically
• tables
• a managed area for hosting tables of all formats in Spark
• e.g. CSV, Parquet, or Delta
• all tables are recognized as tables in the lakehouse
• delta tables are recognized as tables as well
• files 
• an unmanaged area for storing data in any file format [2]
• any Delta files stored in this area aren't automatically recognized as tables [2]
• creating a table over a Delta Lake folder in the unmanaged area requires to explicitly create a shortcut or an external table with a location that points to the unmanaged folder that contains the Delta Lake files in Spark [2]
• ⇐ the main distinction between the managed area (tables) and the unmanaged area (files) is the automatic table discovery and registration process [2]
• {concept} registration process
• runs over any folder created in the managed area only [2]
• {operation} ingest data into lakehouse
• {medium} manual upload
• upload local files or folders to the lakehouse
• {medium} dataflows (Gen2)
• import and transform data from a range of sources using Power Query Online, and load it directly into a table in the lakehouse [1]
• {medium} notebooks
• ingest and transform data, and load it into tables or files in the lakehouse [1]
• {medium} Data Factory pipelines
• copy data and orchestrate data processing activities, loading the results into tables or files in the lakehouse [1]
• {operation} explore and transform data
• {medium} notebooks
•  use code to read, transform, and write data directly to the lakehouse as tables and/or files [1]
• {medium} Spark job definitions
• on-demand or scheduled scripts that use the Spark engine to process data in the lakehouse [1]
• {medium} SQL analytic endpoint: 
• run T-SQL statements to query, filter, aggregate, and otherwise explore data in lakehouse tables [1]
• {medium} dataflows (Gen2): 
• create a dataflow to perform subsequent transformations through Power Query, and optionally land transformed data back to the lakehouse [1]
• {medium} data pipelines: 
• orchestrate complex data transformation logic that operates on data in the lakehouse through a sequence of activities [1]
• (e.g. dataflows, Spark jobs, and other control flow logic).
• {operation} analyze and visualize data
• use the semantic model as the source for Power BI reports 
• {concept} shortcuts
• embedded references within OneLake that point to other files or storage locations
• enable to integrate data into lakehouse while keeping it stored in external storage [1]
• ⇐ allow to quickly source existing cloud data without having to copy it
• e.g. different storage account or different cloud provider [1]
• the user must have permissions in the target location to read the data [1]
• data can be accessed via Spark, SQL, Real-Time Analytics, and Analysis Services
• appear as a folder in the lake
• {limitation} have limited data source connectors
• {alternatives} ingest data directly into your lakehouse [1]
• enable Fabric experiences to derive data from the same source to always be in sync
• {concept} Lakehouse Explorer
• enables to browse files, folders, shortcuts, and tables; and view their contents within the Fabric platform [1]

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References:

[1] Microsoft Learn: Fabric (2023) Get started with lakehouses in Microsoft Fabric (link)

[2] Microsoft Learn: Fabric (2023) Implement medallion lakehouse architecture in Microsoft Fabric (link)